Brooery: A Graphical Environment for
Analysis of Security-Relevant Network Activity
Christian Kreibich
University of Cambridge Computer Laboratory
15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
christian.kreibich@cl.cam.ac.uk
We present the design and implementation of the Brooery, a system for graphical
analysis of network activity reported by instances of the Bro intrusion
detection system. It supports multiple input streams and provides a
web-based graphical user interface to allow the user to analyze the
reported activity. The Brooery understands activity at different
abstraction levels, allows for quick drill-down searches by focusing on
contextuality when moving through the history of events, and provides
user-friendly and semantically strong hierarchical filtering to reduce
the amount of information presented.
In recent years, network monitoring has become a widely adopted practice for
practically every organization interested in understanding the activity
on its networks. Besides other reasons, this is mostly done to improve security:
intrusion detection systems (IDSs) are nowadays widely deployed in order
to help analysts focus their attention on critical events that otherwise
might have been missed in the vast amount of activity.
While these systems have matured a great deal, it has also become clear
that the technology is no silver bullet: in the foreseeable future, the
human element is going to remain an essential component in the analysis
process, largely because our ability to evaluate the relevance of events
in context of other activity is far superior to the one implemented
in present-day technology. This evaluation is rendered more difficult by
the fact that the technology currently does an insufficient job at distilling
the amount of reported activity into a form whose volume is still
comprehensible to humans and at the same time provides all relevant
information necessary to understand the reported event in the full context
of its occurrence.
We believe that much work remains to be done in helping the network analyst in
that task. In this paper we present the Brooery,2 a system to support
the analysis of events reported by the Bro IDS [1].
We base our system on Bro because from the outset, Bro has taken a more differentiated
approach to the detection problem than other IDSs, by separating policy
(i.e., what events to report) from mechanism
(i.e., how to extract the basic building blocks of events from the network).
This separation turns out to be crucial in the analysis process, because the
difference between relevant events and noise is often entirely defined by a site's
policy. By deploying a monitoring policy in line with our understanding of
relevance, we can reduce the volume of reported events from the outset.
The Brooery presents events to the analyst through a graphical user
interface. It allows quick drill-down to relevant details
by allowing the analyst to switch
between different log types, by the use of contextual navigation techniques,
and by employing semantically strong hierarchical filtering that does not
require external skills (such as SQL proficiency) from the analyst.
We first recapture Bro's current features in Section 2 to give the
reader an intuition of the system the Brooery interfaces with. We
present our requirements for the system in Section 3
before describing in detail our resulting architecture along with
implementation details in Section 4. We then exemplify
the application of our system in Section 5 and
review related work in Section 6. The current state
of the system and avenues for future work are discussed in Section
7 before we summarize the paper in Section
8.
Bro: A Distributed Event-Based Intrusion Detection System
Bro's architecture has remained faithful to the philosophy developed
in the original paper [1]. A significant recent
improvement has been the introduction of a communications framework
as the basis of a more powerful event model suitable for distributed
event communication [2,3]. Figure 1
illustrates Bro's architecture.
A core idea of Bro is to split event detection mechanisms from event
processing policies. Event generation is performed by analyzers in
Bro's core: these analyzers operate continuously and trigger events
asynchronously when relevant activity is
observed. Examples include the establishment of a new TCP connection, or
the request for a URL in an HTTP request. Bro's core contains analyzers
for a wide range of network protocols such as TCP, UDP, FTP, HTTP, ICMP,
SMTP, RPC, and others. Care is taken to minimize CPU load: only analyzers
responsible for triggering the events used at the policy layer are actually
enabled.
Bro also provides a bidirectional signature engine for
typical misuse-based intrusion detection: it matches byte string
signatures against traffic flows and triggers an event whenever a signature
matches [4].
Once an event is triggered, the engine passes it to the policy layer.
Each Bro peer runs a policy configuration in its policy layer. This policy embodies
the site's security policy, expressed in scripts containing statements in the
special-purpose Bro scripting language. The language is strongly typed, procedural
in style, and provides a wide range of elementary data types to facilitate the
analysis of activity on a network. The policy layer maintains a large variety of
state information about the activity currently observed on the network. For
each event type, one or more event handlers are triggered that process
events, possibly triggering new ones. Event types are defined by a name and
a set of typed parameters that characterize individual events.
Figure 1:
Architecture of the Bro IDS.
 |
Event handlers may decide to log an event to persistent storage
in a suitable machine- or human-readable format for later analysis. Bro's log
management facility currently comprises two stages. First, at the policy
level, Bro supports a basic notion of log files. These logs can be opened,
closed, and printed to using printf-inspired functions. For notices
and alarms, separate Bro policies take care of formatting the events appropriately
before writing them out. The format of these entries is human-readable, but
sufficiently structured for easy parsing. Second, log rotation is configured
within the policy layer as well. This takes care of archiving the logs:
rotation intervals, monitoring log file size and duration, labelling with start-
and end timestamps, and file compression are all taken care of from within the
policy layer.
Bro's communication framework supports the serialization and transmission
of arbitrary kinds of state between Bro instances. The driving idea
behind its design is to allow the realization of independent state [2]:
we should no longer think of state accumulated
at the policy layer as a local concept, but rather as information
dispersed and stored throughout the network. The communication model
imposes no hierarchical structure. Examples of exchangeable
state include triggered events, state kept in policy data structures,
and the policy definitions themselves. For the purpose of this
paper it is sufficient to think of the entities exchanged between peers
as events, though that ignores a large part of its flexibility.
To interface other applications with Bro, we have implemented a
lightweight, highly portable library supporting Bro's communication
protocol called Broccoli,3 that allows nodes which
are not instances of the Bro IDS to partake in its event communication.
Broccoli nodes can request, send, and receive Bro events just like Bro
itself, but cannot be configured using Bro's policy language. A
Broccoli node's policy has to be implemented in the client's code
or through mechanisms such as configuration files.
The Brooery's Requirements
Figure 2:
The Brooery's three-tiered Architecture.
 |
We have identified the following set of requirements for our system:
- INTEROPERABILITY WITH BRO:
The system should not require significant changes to Bro itself. Existing
communication mechanisms should be leveraged as much as possible.
- FOCUS ON INVESTIGATION:
The primary goal of the system is to enable the analysis of log archive
content using a graphical interface, not to provide a real-time alert
notification system. A highly interactive user interface, while clearly
desirable, is thus not a primary requirement.
- EXPERIMENTAL PROTOTYPING:
Support for rapid prototyping and experimentation with visualization
techniques is more important at this stage than performance optimizations
and long-term maintainability.
- FLEXIBLE FILTERING:
The predominant problem in the analysis of network activity is the total
volume of information. For this reason, effective filtering is essential.
The analysis of Bro's log files has so far mostly happened at the shell prompt,
and the effectiveness was essentially defined by the analyst's command of the typical
text processing toolset: grep, awk, sed, and Perl, just to name few. Skilled
use of these tools, while often unintuitive to other analysts, can be quite
effective. The system should therefore aim at supporting this mindset in
its filter management.
- CONTEXTUALITY:
The richness and diversity of events in Bro requires great flexibility
from an analysis environment. The visual navigation should naturally guide
the user at all times depending on the context of the currently inspected
events, and provide mechanisms for quick drill-down to allow the analyst to
focus on the relevant activity.
- EASY ACCESSIBILITY:
At present, Bro is in day-to-day use throughout several large organizations
around the planet. Such deployments require analyst access from multiple
locations and using different platforms. We therefore prefer a standardized
and widely available rendering mechanism that requires as little preconfiguration
on the analyst's machine as possible.
- SPATIAL SOURCE INDEPENDENCE:
We would like to be able to select individual Bro nodes as
data sources because we do not want to require that all data logging
happen at a single place in the network.
- REPRESENTATIONAL SOURCE INDEPENDENCE:
Bro has traditionally created a set of text-based log files in order to
record events for long-term storage. Two other forms of data storage
are standard database back-ends and live state contained in running Bro
nodes. We would like the system to support access to these uniformly.
- DIFFERENT USER SOPHISTICATION LEVELS:
While Bro's design allows for much flexibility in its configuration, this
freedom also means that its users need to spend more time to familiarize
themselves with the system before they can use it efficiently and effectively.
The user interface should support users of a wide range of sophistication
levels, ranging from the occasional log inspection to operators who are
intimately familiar with Bro policy development and day-to-day Bro maintenance.
Threat Model
The Brooery's main purpose is to present highly security-relevant
information to the analyst, whose conclusions may have severe consequences
for the operation of the network. We therefore need to be aware of
avenues attackers can follow in order to fool the analyst with false
positives and negatives: an attacker might try to hide successful
break-ins from the system, or equally dangerously, have the system report that
a break-in did succeed where in fact there was none. The former causes the
analyst to miss crucial information, while the latter would constitute a
denial of service attack if for example machines had to be taken off line
for investigation and recovery. The main attack surfaces are listed in the
following and need to be protected carefully by the system:
- At the source. The log entry archives could be manipulated directly,
introducing fake events or removing existing ones. Note that this is
different from the typical case where IDSs are tricked into false
positives or negatives; here, the storage system itself is subverted.
- In transit. Log entries need to be transferred and potentially
filtered on their way from the archive to the analyst's console. An attacker
with full control over the involved network flows could drop and introduce
events at will, if the flows are not protected from tampering.
- During filtering & rendering. The system needs to process and
filter log entries for presentation to the analyst. Similar to transit,
an attacker who can modify the way the system itself operates
can drop and introduce information or just cause the system in general to fail.
- At the destination. Since the application is mainly intended to read,
process, and visualize existing information, the main benefit an attacker
would gain from having access to the analyst's console is insight into
what activity Bro nodes have been monitoring. When the system also permits
the analyst to take administrative measures by updating running Bro nodes
from the console, attackers with sufficient privileges to assume the
role of an administrator could disable or attempt to crash individual
Bro nodes.
The Brooery's Architecture
Given the requirements just outlined, we decided to implement our
system in the three-tiered architecture illustrated in Figure
2. Analysts access the system through web
browsers. The web server's back-end implements the core of the
application, taking care of
user interface rendering, user management, data source communication,
and most importantly, the actual log entry processing. We will now
discuss each of these components in more detail.
By using a web-based interface, we do away with the need to deploy
stand-alone client applications, while at the same time avoiding any
porting overhead that such an application might entail. Web-based
interfaces fall short of the interactivity of full-blown client-side
applications unless they employ heavy-weight Java Applets or typically
unrobust JavaScripts. We want to avoid the use of such features to
keep the list of requirements on the client side as small as possible.
However, as outlined in the our requirements, the system is primarily
meant as an analysis tool for investigation of past activity and
not per se as a real-time alert notification tool.
Furthermore,
the web-based interface has the obvious advantage that external
web-based services can be leveraged immediately through HTML linkage,
for example to provide vulnerability information,4common TCP/UDP port usage,5or reports of scanning activity.6We have implemented the web front-end using the Open Source web site
development framework Mason7and the Apache web server.
This allowed us to (i) stay within the same language in which
we have already accumulated a considerable amount of log analysis
code and experience (see Section 4.4 below), and
(ii) employ more advanced language features and mechanisms for
structuring the components of the generated web pages than provided
by other popular web site development solutions like
for example PHP.8
The Brooery is designed to support multiple communication back-ends for
communicating with log archives in the form of text file repositories,
databases, or live Bro agents. Log archives can reside on remote machines
or locally.
While log entry archives are clearly only useful for mining
past activity, the third mechanism is useful for more general
purposes. For example, it could be used to request resource usage summaries
from running Bro instances (suitable policies are already part of the
Bro distribution), or to adjust their current policies dynamically.
All of this would happen within Bro's existing communications framework,
making this approach potentially very powerful.
Management of user sessions
We support access to the system by multiple users at all times. For
each user, the system stores his or her current analysis context,
including log entries, filtering combinations, inspection time frames,
and general user preferences. We currently maintain user identities in
the form of user name and perform authentication using passphrases.
User identities are used to present returning users with the environment
they left earlier.
Log entry processing & filtering
This component is the core of the system and provides the domain knowledge
necessary to manipulate Bro events and log entries. It is implemented
in Perl, for four main reasons:
first, a large body of well-maintained Perl
modules has already been developed in the general context of the Bro project,
so we can instantly leverage these efforts.
Second, Perl is well suited for rapid prototyping, which we feel is
very much the correct development philosophy at this point in time.
Third, the CPAN Perl archive offers
a vast set of modules for any kind of extension we are likely to need in the
future.
Fourth, it is easy to create bindings from Perl to native C, should the need
arise. This could happen for purposes such as performance optimization, or
integration of other components.
The API for log entry retrieval hides the details of the underlying mechanism.
For the log processing engine, a log archive is structured into different
log types representing the different logging domains and abstraction
levels at which Bro reports events (e.g., connection summaries, notices,
signature matches, or alarms). Each log type's archive is comprised of a set
of log slices, each labelled with a start- and end timestamp identifying
the timeframe it covers. Within a log slice, log entries can
be obtained subject to a filtering condition (see Section 4.4.3).
Each back-end implementation maps these abstractions to the actual API available
for accessing a particular log archive.
For accessing text file repositories, we have developed a simple
log entry server that the corresponding Brooery back-end communicates with.
This resembles in many ways a poor man's database implementation; we stress again
that our focus at this point is not on obtaining optimal performance
but getting a good feeling for the problem setting.
Figure 3:
Recursive filter combination using AND/OR-trees and types log
entry components: the main filter matches if both the left and right
subfilters match, the left subfilter matches if the source IP of
an entry belongs to external networks and the destination IP is inside
the DMZ, and the right subfilter matches when the message string of
a log entry contains ``CodeRed'', ``Slammer'', or ``Nimda''.
 |
Our current interaction model requires the user to start the investigation
by selecting a timeframe of activity on which to focus investigation. This
timeframe defines a lower and upper bound on temporal relevance of log entries,
across different log entry types. After selecting a particular log type,
the system then obtains a list of log slices that contain log entries during
the configured timeframe. From these log slices, up to a given maximum number
of entries are then requested starting from a given timestamp. Within the
configured timeframe, the user can then step forward and backward through
the log entries, manipulating a configurable maximum number of log entries at
any one time.
A Type System for Log Entry Components
In our log entry model, every component of a log entry has a type,
inspired by the types provided by the Bro scripting language. The Brooery's type
structure is richer than Bro's and more hierarchical: at the very least, every
log entry component is of the root type ``data'' which provides textual operators
such as ``contains'' and ``does not contain''. A large number of different
types derive from this root, for example timestamps, flow sizes, port numbers,
IP addresses, and protocol names. Further specializations exist for example for
source and destination IP addresses.
The benefits we gain from adhering to such a type model throughout all of our
log types are associativity, extensibility, and semantic processing:
regardless of the type of log we are currently investigating, a source
IP address in one log file type will semantically represent the same as a source IP
address in a different log type. This allows easy integration of future log
types because only novel component types need to be integrated in the type
hierarchy. The only information required to support a new log type is the sequence
of the components' types. Furthermore, knowing that a log entry component
represents for example a timestamp allows us to perform according operations
on the component, in this case for example operations such as ``earlier-than'',
``after'', or ``between''.
Figure 4:
View of a Bro node's log archive.
 |
Figure 5:
Alarm log entries, filtering the ``Alarm'' column for entries containing ``Scan''.
 |
Recursively Reusable AND/OR-Trees for Log Entry Filtering
The Brooery supports an elaborate concept of log entry matching based on
AND/OR-trees, known from other applications such as attack trees [5].
The Brooery combines the expressiveness of conditions using AND/OR-trees
with the strengths of the typed log entry components. For example,
timestamps can be matched depending on whether they represent time
earlier or later than a given timestamp, and IP addresses can be tested
for (not) matching an address prefix.
A filter always consists of one ore more filter parts, each of
which can either contain a filtering criterion as just described, or
refer to another existing filter. The filtering results of all filter
parts are then combined using Boolean conjunctions or disjunctions
and lazy evaluation. Cyclic dependency detection prevents the user from
configuring self-referring constructs. This approach to filter management
allows the creation of arbitrarily nested filtering hierarchies,
while ensuring easy re-use of existing filters. Figure 3
illustrates the concepts.
Note that a filter so far only represents a focusing mechanism;
``filtering'' is not meant to imply ``dropping'' at this point. Dropping a log
entry due to successful filter evaluation is merely one of a range of
conceivable filter actions; other examples include keeping a log
entry, labelling it, and aggregating log entries by compressing
all entries that are matched by a filter into a single abstract
entry. We can summarize the aggregation by reporting in an abstract
entry the number of entries it represents, while maintaining the
common parts of those log entries visible.
The fact that multiple filters can trigger actions of different semantic
meaning does imply that multiple filters may need to be active at the same
time. For example, one filter could throw out unwanted entries, another one
could aggregate the remaining ones. Integrating the output of
multiple filters brings the potential of conflict: for example, one
filter could declare that an entry is to be dropped while a second one asks
for it to be kept. The way we solve this problem is through ordering:
while the user may have multiple filters active at the same time,
those filters have to be put in a sequence, and the first decision
made in a conflict domain is decisive.
To allow the user to quickly weed out unwanted information and focus on
the interesting entries, the Brooery also supports incremental filtering,
i.e., the addition of new filter parts to a selected filter. At no time
does the nature of the log entry storage shine through; for example,
the user never has to resort to entering raw database queries.
As outlined by our threat model, care must be taken to restrict the user
base of the system to the intended individuals while preventing others
from eavesdropping on or even tampering with the information flows.
First of all, we assume that when an attacker manages to break into
one of the hosts running Bro, or one of the machines storing a log
archive, it is unlikely that we can prevent a determined intruder from
causing serious damage to the system. Therefore, the security precautions
of Bro nodes and log archives remain unchanged, regardless of whether
these systems are interfaced with the Brooery or not.
Regarding the communication between the various nodes interacting with
the Brooery, we use two different levels of restrictiveness: inside the Bro network,
we can assume that the communicating entities know each other's identities.
Besides employing SSL encryption, we can therefore require mutual
authentication of the communicating peers through certificates. The web
browsers accessing the system are still required to use encrypted connections via
HTTPS, but as mentioned in Section 4.3 we drop the
requirement for client-side certificates and resort to weaker
authentication in the form of user names & passphrases. Furthermore,
we aim to restrict the reachability of the involved hosts to a minimum
whenever possible, for example by only making the ports on which Bro data
sources can be tapped available on a separate network.
Usage Example
We will now give a quick but illustrative example of user interaction
with the Brooery. Let us a assume that we have been informed
that on October 10, several users have noticed unusual connection attempts to
their machines. Our goal now is to find out whether any relevant scanning activity
was detected that day. Figure 4 shows a Bro node's log archive,
and we can see that the node has plenty of information for October 10: there
are 7 different log types ranging from alarms over connection summaries to
worm events, reaching in time from the beginning of September to the present
at the time the screenshot was taken. Horizontal blue bars indicate the timeframes
during which the Bro node was logging events of a particular type.
The blue color is differentiated into two different shades, with each color
switch indicating the start of a new log slice. Note that
the bars cover the timeframe when the IDS was ready to log, not the
timeframe from the first logged event to the last one -- an important semantical
difference. For example, we can see that the system was not monitoring at all
for several hours on October 16 and 18, and that worm event logging
was introduced on September 27. The small vertical orange lines delineate
compressed from uncompressed archival, i.e., log entries residing on the
left side of an orange line are stored in compressed fashion.
We specify
a time interval covering that day, and look at the contents of the
alarms log. We then add a filter on the alarm names that matches all entries
containing ``Scan'', and obtain the result shown in Figure 5.
As can be seen, several hosts have scanned a large number of machines,
and we can now continue our analysis by looking at the connection
summaries for each of those hosts during the given time period. To
do so, we click on any of the shown IP addresses, add a filter part
for that address using the option the context panel for IP addresses provides
for this purpose, and switch the log type to the connection logs.
Related Work
In [6], Hoagland and Staniford proposed SnortSnarf, a
web-based console for analyzing Snort alerts [7].
ACID9 is similar and additionally
allows the generation of charts and statistics. Our system is superficially
similar to these, however our system is more comprehensive in that it
(i) can manage a wider variety log types and (ii) structures log
entries more thoroughly due to typed column entries for stronger semantic
filtering across different log types.
Sguil10 is close to our system in
the sense that it acknowledges the need for providing contextual information
for alerts such as connection logs and packet content. Our system differs from
theirs in that filter management in Sguil is less intuitive for the user
(who has to resort to SQL statements); also, Sguil is implemented in Tcl/Tk and
therefore not as readily accessible as our web-based interface.
A number of other user interfaces for Snort exist; they are typically
geared towards support for signature management and do not provide the
flexibility to deal with the wide range of log information provided
by Bro.
In the commercial space, user interfaces are often bundled with
IDS products directly or offered in the general Security Incident Management
domain. As our focus is on open-sourced solutions, we do not review the
commercial domain thoroughly in the scope of this paper.
Discussion & Future Work
The Brooery is work in progress and a prototyping testbed. We use it
for experimentation with different models for analyzing log information
and therefore feel it is important to point out that we are currently
primarily interested in different metaphors for manipulating the log
information; aspects such as performance optimization
remain secondary. So far we have found the graphical instruments
realizable using HTML sufficient for our needs; it will be interesting
to see if this observation will apply to future extensions to the system
as well.
We currently see two main avenues for future work. First, we need to augment
Bro with a database logging component that does not require fundamental
modifications of Bro's logging component. Database-driven archival is very
much a necessity for robust log entry storage & retrieval, and, of course,
performance. Text-file based storage, while familiar and to a certain degree
manageable at the command line, restricts performance and can sometimes pose
technical difficulties.
One avenue we are considering for achieving this is to turn the act of logging an event into
an event itself. That way, the implementation of the logging mechanism would
remain up to individual event handlers, could happen in multiple ways in
parallel, and other event logging systems (including the Brooery) could
tap into the stream of logged events using e.g. Broccoli and the existing
event communications framework. This approach would thus fit very nicely
into Bro's model. Depending on the implementation of the event handlers
responsible for processing such logging events, the events would then be
stored in a text file, a database, or processed in some other way. The Brooery's
log entry model is geared towards easy mapping onto relational structures; the
main difference her are the semantically stronger types used at within the
log entry engine.
Second, we intend to investigate the requirements for effective analysis
of distributed events. We are currently correlating events originating on
multiple Bro nodes using Bro's event communication framework; however, it
is not yet clear to us what will turn out to be the best visual metaphor
for controlling this correlation and visualizing the results.
Summary
We have presented the Brooery, a three-tiered experimental prototyping
platform for graphical analysis of network activity reported by instances
of the Bro IDS. The system provides contextually relevant drill-down features
and supports different Bro log archival back-ends; semantically strong and
reusable log entry matching based on AND/OR trees; filtering, labelling, and
aggregation of log entries; and hierarchically typed log entry components.
The Brooery's development is fully open sourced under a BSD license. More
details can be found at http://www.icir.org/twiki/bin/view/Bro/BrooeryGUI.
Acknowledgements
This work is carried out in collaboration with Intel Research Cambridge,
ICIR, and Lawrence Berkeley National Laboratory. It was supported in part
by the U.S. National Science Foundation grant STI-0334088, and the U.S.
Department of Energy. We would like to thank Vern Paxson, Jon Crowcroft,
and the other Bro developers for helpful discussion and feedback, and
the occasional brainstorming in real-world ``brooery'' environments.
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Vern Paxson.
Bro: A System for Detecting Network Intruders in Real-Time.
Computer Networks (Amsterdam, Netherlands: 1999),
31(23-24):2435-2463, 1998.
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Robin Sommer and Vern Paxson.
Exploiting Independent State For Network Intrusion Detection.
Technical Report TUM-I0420, TU München, 2004.
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Christian Kreibich and Robin Sommer.
Policy-controlled Event Management for Distributed Intrusion
Detection.
In Proceedings of the 4th International Workshop on Distributed
Event-Based Systems (DEBS'05), June 2005.
- 4
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Robin Sommer and Vern Paxson.
Enhancing Byte-Level Network Intrusion Detection Signatures with
Context.
In Proc. 10th ACM Conference on Computer and Communications
Security, 2003.
- 5
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Bruce Schneier, editor.
Secrets and Lies, chapter 21, pages 318-333.
John Wiley and Sons, New York, 2000.
- 6
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James A. Hoagland and Stuart Staniford.
Viewing IDS alerts: Lessons from SnortSnarf.
Technical report, Silicon Defense, Nov 2000.
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Martin Roesch.
Snort: Lightweight Intrusion Detection for Networks.
In Proceedings of the 13th Conference on Systems
Administration, pages 229-238, 1999.
Brooery: A Graphical Environment for
Analysis of Security-Relevant Network Activity
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Copyright © 1997, 1998, 1999,
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Footnotes
- ...
\let
-
- ...Brooery,2
- ``Brooery'' is the
short form of the full title, Bro Observatory.
- ...Broccoli,3
- Broccoli is the healthy
acronym for ``Bro Client Communications Library'', available at
http://www.cl.cam.ac.uk/~cpk25/broccoli/
- ... information,4
- http://cve.mitre.org
- ... usage,5
- http://www.treachery.net/security_tools/ports/
- ... activity.6
- http://www.dshield.org
- ... Mason7
- http://www.masonhq.com
- ... PHP.8
- http://www.php.net
- ...
ACID9
- http://acidlab.sourceforge.net
- ...
Sguil10
- http://sguil.sourceforge.net
Christian Kreibich
2005-02-25